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                        Introduction
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                            <b>1.</b>
                        
                        第一章：数据分析前奏
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                            <b>1.1.</b>
                        
                        第1节：什么是数据分析
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                            <b>1.2.</b>
                        
                        第2节：环境搭建
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                            <b>1.3.</b>
                        
                        第3节：jupyternotebook使用
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                            <b>1.4.</b>
                        
                        第4节：作业
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                            <b>2.</b>
                        
                        第二章：Numpy库
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                            <b>2.1.</b>
                        
                        第1节：numpy库介绍
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                            <b>2.2.</b>
                        
                        第2节：numpy数组基本
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                            <b>2.3.</b>
                        
                        第3节：numpy数组操作
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                            <b>2.4.</b>
                        
                        第4节：numpy索引和切片
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                            <b>2.5.</b>
                        
                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                            <b>2.7.</b>
                        
                        第7节：深拷贝和浅拷贝
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                            <b>2.8.</b>
                        
                        第8节：文件操作
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                            <b>2.9.</b>
                        
                        第9节：数组操作和文件操作作业.md
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                            <b>2.10.</b>
                        
                        第10节：NAN和INF值处理.md
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                            <b>2.11.</b>
                        
                        第11节：random模块.md
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                            <b>2.12.</b>
                        
                        第12节：axis理解.md
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                            <b>2.13.</b>
                        
                        第13节：通用函数.md
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                            <b>2.14.</b>
                        
                        第14节：numpy练习题.md
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                            <b>3.</b>
                        
                        第三章：Pandas库
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                            <b>3.1.</b>
                        
                        第1节：pandas介绍
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                            <b>3.2.</b>
                        
                        第2节：pandas索引操作
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                            <b>3.3.</b>
                        
                        第3节：pandas对齐运算
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                            <b>3.4.</b>
                        
                        第4节：pandas函数应用
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                            <b>3.5.</b>
                        
                        第5节：pandas层级索引
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                            <b>3.6.</b>
                        
                        第6节：pandas统计计算和描述
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                            <b>3.7.</b>
                        
                        第7节：文件操作
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                            <b>3.8.</b>
                        
                        第8节：数据清洗
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                            <b>3.9.</b>
                        
                        第9节：聚合和分组
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                            <b>3.10.</b>
                        
                        第10节：时间序列
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                            <b>4.</b>
                        
                        第四章：Matploblib库
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                            <b>4.1.</b>
                        
                        第1节：常用图
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                            <b>4.2.</b>
                        
                        第2节：基本使用
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                            <b>4.3.</b>
                        
                        第3节：条形图
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                            <b>4.4.</b>
                        
                        第4节：直方图
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                            <b>4.5.</b>
                        
                        第5节：散点图
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                            <b>4.6.</b>
                        
                        第6节：饼图
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                            <b>4.7.</b>
                        
                        第7节：箱线图
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                            <b>4.8.</b>
                        
                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                            <b>5.2.</b>
                        
                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
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                            <b>6.6.</b>
                        
                        第6节：分布分析
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                            <b>6.7.</b>
                        
                        第7节：交叉分析
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                            <b>6.8.</b>
                        
                        第8节：统计分析
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                            <b>6.9.</b>
                        
                        第9节：帕累托分析
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                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
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                            <b>6.11.</b>
                        
                        第11节：综合性分析
                    </a>
            
            
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            </ul>
            
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                            <b>7.</b>
                        
                        第七章：数据分析实战
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                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
                    </a>
            
            
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                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
                    </a>
            
            
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        <li class="chapter " data-level="7.3" data-path="chapter6/03StackoverFlow数据分析.html">
            
                
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                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
                    </a>
            
            
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        <li class="chapter " data-level="7.4" data-path="chapter6/04二手房数据分析.html">
            
                
                    <a href="../chapter6/04二手房数据分析.html">
                
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                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
                    </a>
            
            
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        <li class="chapter " data-level="7.5" data-path="chapter6/05吃鸡数据分析.html">
            
                
                    <a href="../chapter6/05吃鸡数据分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
                    </a>
            
            
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        <li class="chapter " data-level="7.6" data-path="chapter6/06黑色星期五数据分析.html">
            
                
                    <a href="../chapter6/06黑色星期五数据分析.html">
                
                        <i class="fa fa-check"></i>
                        
                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
                    </a>
            
            
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            </ul>
            
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                            <b>8.</b>
                        
                        第八章：补充
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                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
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                            <b>8.2.</b>
                        
                        第2节：echarts和pyecharts库
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                            <b>8.3.</b>
                        
                        第3节：bokeh库
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                        <h1 id="matplotlib&#x4F5C;&#x4E1A;">matplotlib&#x4F5C;&#x4E1A;</h1>
<h2 id="&#x4E00;&#x3001;&#x6298;&#x7EBF;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;">&#x4E00;&#x3001;&#x6298;&#x7EBF;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;</h2>
<ol>
<li>&#x4EE5;&#x4E0B;&#x662F;&#x957F;&#x6C99;&#x67D0;&#x4E00;&#x4E2A;&#x6708;&#x7684;&#x5929;&#x6C14;&#x6570;&#x636E;&#xFF0C;&#x6309;&#x7167;&#x65F6;&#x95F4;&#x7684;&#x987A;&#x5E8F;&#x7ED8;&#x5236;&#x6210;&#x6298;&#x7EBF;&#x56FE;&#xFF0C;&#x5176;&#x4E2D;&#x6570;&#x636E;<code>highest</code>&#x662F;&#x6700;&#x9AD8;&#x6E29;&#x5EA6;&#xFF0C;<code>lowest</code>&#x662F;&#x6700;&#x4F4E;&#x6E29;&#x5EA6;&#x3002;&#x6700;&#x9AD8;&#x6E29;&#x5EA6;&#x7EBF;&#x6761;&#x7528;&#x7EA2;&#x8272;&#xFF0C;&#x6700;&#x4F4E;&#x6E29;&#x5EA6;&#x7EBF;&#x6761;&#x7528;&#x84DD;&#x8272;&#x3002;</li>
<li>&#x5177;&#x4F53;&#x7684;&#x5750;&#x6807;&#x70B9;&#xFF0C;&#x7528;&#x5706;&#x70B9;marker&#x8868;&#x793A;&#x3002;</li>
<li>&#x628A;x&#x8F74;&#x7684;&#x65F6;&#x95F4;&#x523B;&#x5EA6;&#x6309;&#x7167;<code>1-31</code>&#x6807;&#x8BB0;&#x51FA;&#x6765;&#xFF0C;&#x5E76;&#x4E14;&#x6807;&#x8BB0;x&#x8F74;&#x548C;y&#x8F74;&#x7684;&#x6807;&#x9898;&#x3002;</li>
<li>&#x56FE;&#x7684;&#x6807;&#x9898;&#x662F;&#x201C;&#x957F;&#x6C99;5&#x6708;&#x4EFD;&#x6C14;&#x6E29;&#x8D70;&#x52BF;&#x201D;&#x3002;</li>
</ol>
<p><strong>&#x6570;&#x636E;&#xFF1A;</strong></p>
<pre><code class="lang-python">highest = [<span class="hljs-number">26</span>,<span class="hljs-number">21</span>,<span class="hljs-number">26</span>,<span class="hljs-number">26</span>,<span class="hljs-number">22</span>,<span class="hljs-number">20</span>,<span class="hljs-number">17</span>,<span class="hljs-number">19</span>,<span class="hljs-number">22</span>,<span class="hljs-number">28</span>,<span class="hljs-number">30</span>,<span class="hljs-number">28</span>,<span class="hljs-number">24</span>,<span class="hljs-number">28</span>,<span class="hljs-number">25</span>,<span class="hljs-number">26</span>,<span class="hljs-number">25</span>,<span class="hljs-number">26</span>,<span class="hljs-number">25</span>,<span class="hljs-number">23</span>,<span class="hljs-number">24</span>,<span class="hljs-number">30</span>,<span class="hljs-number">32</span>,<span class="hljs-number">31</span>,<span class="hljs-number">30</span>,<span class="hljs-number">27</span>,<span class="hljs-number">26</span>,<span class="hljs-number">27</span>,<span class="hljs-number">29</span>,<span class="hljs-number">25</span>,<span class="hljs-number">25</span>]
lowest =  [<span class="hljs-number">17</span>,<span class="hljs-number">13</span>,<span class="hljs-number">17</span>,<span class="hljs-number">18</span>,<span class="hljs-number">18</span>,<span class="hljs-number">17</span>,<span class="hljs-number">14</span>,<span class="hljs-number">15</span>,<span class="hljs-number">16</span>,<span class="hljs-number">18</span>,<span class="hljs-number">19</span>,<span class="hljs-number">20</span>,<span class="hljs-number">18</span>,<span class="hljs-number">18</span>,<span class="hljs-number">20</span>,<span class="hljs-number">20</span>,<span class="hljs-number">20</span>,<span class="hljs-number">20</span>,<span class="hljs-number">20</span>,<span class="hljs-number">16</span>,<span class="hljs-number">17</span>,<span class="hljs-number">19</span>,<span class="hljs-number">21</span>,<span class="hljs-number">24</span>,<span class="hljs-number">24</span>,<span class="hljs-number">23</span>,<span class="hljs-number">20</span>,<span class="hljs-number">18</span>,<span class="hljs-number">19</span>,<span class="hljs-number">18</span>,<span class="hljs-number">19</span>]
</code></pre>
<p><strong>&#x6548;&#x679C;&#x56FE;&#x53C2;&#x8003;&#xFF1A;</strong><br><img src="../assets/homework/1&#x6298;&#x7EBF;&#x56FE;&#x4F5C;&#x4E1A;&#x6548;&#x679C;&#x56FE;.png" alt=""></p>
<h2 id="&#x4E8C;&#x3001;&#x6761;&#x5F62;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;">&#x4E8C;&#x3001;&#x6761;&#x5F62;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;</h2>
<ol>
<li>&#x4EE5;&#x4E0B;&#x6570;&#x636E;&#x662F;&#x4E09;&#x7C7B;&#x5B66;&#x6821;&#xFF08;&#x666E;&#x901A;&#x672C;&#x79D1;&#x3001;&#x4E2D;&#x7B49;&#x804C;&#x4E1A;&#x6559;&#x80B2;&#x3001;&#x666E;&#x901A;&#x9AD8;&#x4E2D;&#xFF09;&#x5728;2014-2018&#xFF08;&#x5305;&#x542B;2018&#xFF09;&#x7684;&#x62A5;&#x540D;&#x4EBA;&#x6570;&#xFF0C;&#x7528;DataFrame&#x6784;&#x5EFA;&#x3002;</li>
<li>&#x628A;&#x5E74;&#x4EFD;&#x5F53;&#x505A;x&#x8F74;&#xFF0C;&#x62A5;&#x540D;&#x4EBA;&#x6570;&#x5F53;&#x505A;y&#x8F74;&#x7684;&#x503C;&#x3002;</li>
<li>&#x7ED8;&#x5236;&#x5206;&#x7EC4;&#x6761;&#x5F62;&#x56FE;&#xFF0C;&#x540C;&#x4E00;&#x4E2A;&#x5E74;&#x4EFD;&#x7684;&#x653E;&#x5728;&#x4E00;&#x4E2A;&#x7EC4;&#x3002;</li>
<li>&#x56FE;&#x4F8B;&#x6A2A;&#x5411;&#x6392;&#x5217;&#xFF08;&#x63D0;&#x793A;&#xFF1A;&#x7528;legend&#x7684;ncol&#x53C2;&#x6570;&#xFF0C;ncol&#x8868;&#x793A;&#x7684;&#x662F;&#x628A;&#x56FE;&#x4F8B;&#x5206;&#x6210;&#x591A;&#x5C11;&#x5217;&#x663E;&#x793A;&#xFF09;&#x3002;</li>
<li>&#x628A;&#x62A5;&#x540D;&#x4EBA;&#x6570;&#x5728;&#x56FE;&#x4E0A;&#x7ED8;&#x5236;&#x51FA;&#x6765;&#x3002;</li>
</ol>
<p><strong>&#x6570;&#x636E;&#xFF1A;</strong></p>
<pre><code class="lang-python">data = {
    <span class="hljs-string">&quot;&#x666E;&#x901A;&#x672C;&#x79D1;&quot;</span>:[<span class="hljs-number">721</span>,<span class="hljs-number">738</span>,<span class="hljs-number">749</span>,<span class="hljs-number">761</span>,<span class="hljs-number">791</span>],
    <span class="hljs-string">&quot;&#x4E2D;&#x7B49;&#x804C;&#x4E1A;&#x6559;&#x80B2;&quot;</span>: [<span class="hljs-number">620</span>,<span class="hljs-number">601</span>,<span class="hljs-number">593</span>,<span class="hljs-number">582</span>,<span class="hljs-number">557</span>],
    <span class="hljs-string">&quot;&#x666E;&#x901A;&#x9AD8;&#x4E2D;&quot;</span>: [<span class="hljs-number">797</span>,<span class="hljs-number">797</span>,<span class="hljs-number">803</span>,<span class="hljs-number">800</span>,<span class="hljs-number">793</span>]
}
</code></pre>
<p><strong>&#x6548;&#x679C;&#x56FE;&#x53C2;&#x8003;&#xFF1A;</strong><br><img src="../assets/homework/2&#x6761;&#x5F62;&#x56FE;&#x4F5C;&#x4E1A;&#x6548;&#x679C;&#x56FE;.png" alt=""></p>
<h2 id="&#x4E09;&#x3001;&#x76F4;&#x65B9;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;">&#x4E09;&#x3001;&#x76F4;&#x65B9;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;</h2>
<ol>
<li>&#x7528;pandas&#x4ECE;scores.csv&#x8BFB;&#x53D6;&#x51FA;&#x6765;&#xFF0C;&#x5F62;&#x6210;&#x4E00;&#x4E2A;DataFrame&#x5BF9;&#x8C61;&#x3002;</li>
<li>&#x7ED8;&#x5236;&#x5316;&#x5B66;&#x6210;&#x7EE9;&#x7684;&#x76F4;&#x65B9;&#x56FE;&#xFF08;chem&#x5217;&#xFF09;&#x3002;</li>
<li>&#x6807;&#x8BB0;x&#x8F74;&#x7684;&#x5750;&#x6807;&#x3002;</li>
<li>&#x6807;&#x8BB0;&#x6BCF;&#x4E2A;&#x6761;&#x5F62;&#x4E0A;&#x7684;&#x5177;&#x4F53;&#x6570;&#x503C;&#x3002;</li>
</ol>
<p><strong>&#x6570;&#x636E;&#xFF1A;</strong><br>&#x5728;<code>matplotlib&#x4EE3;&#x7801;-&gt;&#x4F5C;&#x4E1A;&#x53C2;&#x8003;</code>&#x6587;&#x4EF6;&#x5939;&#x7684;<code>scores.csv</code>&#x6587;&#x4EF6;&#x4E2D;&#x3002;</p>
<p><strong>&#x6548;&#x679C;&#x56FE;&#x53C2;&#x8003;&#xFF1A;</strong><br><img src="../assets/homework/3&#x76F4;&#x65B9;&#x56FE;&#x4F5C;&#x4E1A;&#x6548;&#x679C;&#x56FE;.png" alt=""></p>
<h2 id="&#x56DB;&#x3001;&#x6563;&#x70B9;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;">&#x56DB;&#x3001;&#x6563;&#x70B9;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;</h2>
<ol>
<li>&#x628A;guazi_bj&#xFF08;&#x5317;&#x4EAC;&#xFF09;&#x3001;guazi_gz&#xFF08;&#x5E7F;&#x5DDE;&#xFF09;&#x3001;guazi_sh&#xFF08;&#x4E0A;&#x6D77;&#xFF09;&#x3001;guazi_sz&#xFF08;&#x6DF1;&#x5733;&#xFF09;&#x4E8C;&#x624B;&#x8F66;&#x7684;&#x6570;&#x636E;&#x5F52;&#x7C7B;&#x5728;&#x4E00;&#x4E2A;DataFrame&#x4E2D;&#x3002;</li>
<li>&#x65B0;&#x589E;&#x8F66;&#x8F86;&#x4F7F;&#x7528;&#x5E74;&#x4EFD;&#xFF08;use_year&#xFF09;&#x4E0E;&#x4FDD;&#x503C;&#x7387;&#xFF08;hedge_rate&#xFF09;&#x4E24;&#x4E2A;&#x5B57;&#x6BB5;&#x3002;&#x5176;&#x4E2D;&#x4F7F;&#x7528;&#x5E74;&#x4EFD;&#x7684;&#x8BA1;&#x7B97;&#x662F;&#x628A;&#x5F53;&#x524D;&#x7684;&#x65F6;&#x95F4;&#x51CF;&#x53BB;&#x8D2D;&#x4E70;&#x7684;&#x65F6;&#x95F4;&#xFF0C;&#x7136;&#x540E;&#x518D;&#x8F6C;&#x6362;&#x6210;&#x5E74;&#xFF1B;&#x4FDD;&#x503C;&#x7387;&#x7684;&#x8BA1;&#x7B97;&#x662F;&#x5C06;&#x4E8C;&#x624B;&#x8F66;&#x7684;&#x4EF7;&#x683C;/&#x65B0;&#x8F66;&#x7684;&#x4EF7;&#x683C;&#x3002;</li>
<li>&#x628A;&#x4E8C;&#x624B;&#x8F66;&#x4F7F;&#x7528;&#x5E74;&#x4EFD;&#x4E0E;&#x4FDD;&#x503C;&#x7387;&#xFF08;&#x4E8C;&#x624B;&#x8F66;&#x4EF7;/&#x65B0;&#x8F66;&#x4EF7;&#x683C;&#xFF09;&#x7ED8;&#x5236;&#x6210;&#x6563;&#x70B9;&#x56FE;&#xFF0C;&#x89C2;&#x5BDF;&#x4ED6;&#x4EEC;&#x7684;&#x5206;&#x5E03;&#x60C5;&#x51B5;&#x3002;</li>
<li>&#x628A;&#x4E8C;&#x624B;&#x8F66;&#x7684;&#x884C;&#x9A76;&#x8DDD;&#x79BB;&#x4E0E;&#x4FDD;&#x503C;&#x7387;&#xFF08;&#x4E8C;&#x624B;&#x8F66;&#x4EF7;/&#x65B0;&#x8F66;&#x4EF7;&#x683C;&#xFF09;&#x7ED8;&#x5236;&#x6210;&#x6563;&#x70B9;&#x56FE;&#xFF0C;&#x89C2;&#x5BDF;&#x4ED6;&#x4EEC;&#x7684;&#x5206;&#x5E03;&#x60C5;&#x51B5;&#x3002;
<img src="../assets/homework/&#x6563;&#x70B9;&#x56FE;&#x4F5C;&#x4E1A;&#x6548;&#x679C;1.png" alt="">
<img src="../assets/homework/&#x6563;&#x70B9;&#x56FE;&#x4F5C;&#x4E1A;&#x6548;&#x679C;2.png" alt=""></li>
</ol>
<h2 id="&#x4E94;&#x3001;&#x997C;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;">&#x4E94;&#x3001;&#x997C;&#x56FE;&#x4F5C;&#x4E1A;&#x8981;&#x6C42;&#xFF1A;</h2>
<ol>
<li>&#x628A;&#x4EE5;&#x4E0B;&#x6570;&#x636E;&#x7ED8;&#x5236;&#x6210;&#x997C;&#x56FE;&#x3002;</li>
<li>&#x628A;Chrome&#x6D4F;&#x89C8;&#x5668;&#x7684;&#x6A21;&#x5757;&#x5206;&#x5272;&#x5F00;0.05&#x3002;</li>
<li>&#x8BBE;&#x7F6E;&#x9634;&#x5F71;&#x3002;</li>
<li>&#x628A;&#x767E;&#x5206;&#x6570;&#x7684;&#x989C;&#x8272;&#x8BBE;&#x7F6E;&#x6210;&#x767D;&#x8272;&#xFF0C;&#x628A;&#x6D4F;&#x89C8;&#x5668;&#x7684;&#x540D;&#x5B57;&#x989C;&#x8272;&#x8BBE;&#x7F6E;&#x6210;&#x9ED1;&#x8272;&#x3002;</li>
<li>&#x628A;Edge&#x548C;Safari&#x6D4F;&#x89C8;&#x5668;&#x7684;&#x6BD4;&#x4F8B;&#x6587;&#x5B57;&#x5B57;&#x4F53;&#x5927;&#x5C0F;&#x8C03;&#x6210;10&#xFF0C;&#x5176;&#x4ED6;&#x7684;12&#x3002;</li>
</ol>
<p>&#x6548;&#x679C;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/homework/&#x997C;&#x56FE;&#x4F5C;&#x4E1A;&#x6548;&#x679C;&#x56FE;.png" alt=""></p>
<h2 id="&#x516D;&#x3001;&#x7BB1;&#x7EBF;&#x56FE;&#x4F5C;&#x4E1A;&#xFF1A;">&#x516D;&#x3001;&#x7BB1;&#x7EBF;&#x56FE;&#x4F5C;&#x4E1A;&#xFF1A;</h2>
<ol>
<li>&#x8BFB;&#x53D6;scores.csv&#x6587;&#x4EF6;&#x3002;</li>
<li>&#x628A;&#x6240;&#x6709;&#x79D1;&#x76EE;&#x7684;&#x6210;&#x7EE9;&#x90FD;&#x5728;&#x4E00;&#x5F20;&#x56FE;&#x4E0A;&#x7ED8;&#x5236;&#x7BB1;&#x7EBF;&#x56FE;&#x3002;</li>
<li>&#x89C2;&#x5BDF;&#x8FD9;&#x4E2A;&#x56FE;&#xFF0C;&#x4F60;&#x80FD;&#x53D1;&#x73B0;&#x4EC0;&#x4E48;&#x4FE1;&#x606F;&#x3002;</li>
</ol>
<p>&#x6548;&#x679C;&#x56FE;&#xFF1A;
<img src="../assets/homework/&#x7BB1;&#x7EBF;&#x56FE;&#x4F5C;&#x4E1A;&#x6548;&#x679C;&#x56FE;.png" alt=""></p>
<h2 id="&#x4E03;&#x3001;&#x96F7;&#x8FBE;&#x56FE;&#x4F5C;&#x4E1A;&#xFF1A;">&#x4E03;&#x3001;&#x96F7;&#x8FBE;&#x56FE;&#x4F5C;&#x4E1A;&#xFF1A;</h2>
<ol>
<li>&#x8BFB;&#x53D6;scores.csv&#x6587;&#x4EF6;&#x6210;DataFrame&#x5BF9;&#x8C61;&#x3002;</li>
<li>&#x8BA1;&#x7B97;&#x6BCF;&#x4E2A;&#x79D1;&#x76EE;&#x7684;&#x5E73;&#x5747;&#x6210;&#x7EE9;&#x3002;</li>
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